51 results about "High risk environment" patented technology

The invention relates to the technical field of path planning, in particular to a multi-agent dynamic nuclear emergency evacuation path planning method and system based on deep reinforcement learning. In particular to a multi-agent optimization path generation and dynamic adjustment method based on a deep reinforcement learning technology, and aims to improve the real-time performance, the safety and the execution efficiency of a public evacuation path in an emergency nuclear event. According to the scheme, the optimal evacuation path is automatically explored through deep reinforcement learning, a path model does not need to be manually preset, complex factors such as pollution dynamic diffusion and traffic jam under the nuclear accident scene can be dealt with, and the efficiency and safety of emergency evacuation path planning are greatly improved. The system is suitable for public emergency response systems in high-risk environments such as nuclear accidents, earthquakes, fire disasters and urban explosions.

The invention provides a multi-target path planning method and system fusing multi-risk perception and a compensation mechanism. The method comprises the following steps: S1, environment obstacle preprocessing; s2, constructing a first risk map; s3, constructing a second risk map; s4, defining a multi-dimensional path assessment target: setting a path length target and one or more risk assessment targets based on the first risk map and the second risk map respectively; and S5, executing multi-target path planning: searching and outputting a group of Pareto optimal paths between a given starting point and a given target point according to a multi-dimensional path evaluation target by adopting a layered multi-target any-angle path planning algorithm, and realizing risk compensation by balancing each evaluation target through the path planning algorithm. According to the method, risk compensation is internally realized through multi-objective optimization, that is, the path length and different types of risks are balanced, so that the path which is not only short and smooth but also safer in a complex and high-risk environment is planned.

The invention discloses an inspection robot for a special underground environment of a coal mine and a use method, and relates to the technical field of coal mine safety production. The inspection robot for the special environment of the underground coal mine comprises a walking track and further comprises a walking mechanism, a main body frame, a detection unit, a power supply unit, a communication unit and a pan-tilt shield assembly, the walking mechanism is matched with the walking track to achieve movement, the detection unit achieves instrument image collection through the pan-tilt shield assembly, and the power supply unit supplies power to the main body frame. The power supply unit and the communication unit respectively supply power and transmit data to the whole machine; stable operation in a high-risk environment is ensured by adopting an explosion-proof safety design, high-precision positioning and identification of the instrument are realized through a dynamic search algorithm and an intelligent detection algorithm, unmanned inspection and real-time data transmission functions are realized, a modular structure can be flexibly expanded, various instrument detection requirements can be met, and the system is suitable for popularization and application. And the coal mine inspection efficiency and the intelligent level are obviously improved.

The embodiment of the invention discloses a space grid adaptive division method and device, equipment and a medium. According to the embodiment of the invention, the method comprises the steps: obtaining the multi-source heterogeneous three-dimensional data of a target region, carrying out the preprocessing of the multi-source heterogeneous three-dimensional data, and obtaining the vertex density of each vertex in the target region; according to the vertex coordinates of the vertexes, performing prediction based on a pre-trained environment factor continuous expression model to obtain multivariate environment factors corresponding to the vertexes; for any grid in the target area, determining whether the grid is a to-be-divided grid based on the vertex density of each vertex in the grid and the multivariate environment factor; and continuously dividing the grid to be divided based on the longest axis bisection strategy. Therefore, the vertex density and the environmental factors are integrated into the judgment mechanism of the grid to be divided, recursive division is performed through the judgment mechanism, so that a finer grid is obtained in a complex area or a high-risk environment, a thicker grid is maintained in a simple area or a low-risk environment, and non-uniform and differentiated adaptive division is realized.

This application discloses a spatial grid adaptive partitioning method, apparatus, device, and medium. The embodiment acquires multi-source heterogeneous 3D data of a target region and preprocesses the data to obtain the vertex density of each vertex within the target region. Based on the vertex coordinates, a pre-trained continuous environmental factor expression model predicts the multivariate environmental factors corresponding to each vertex. For any grid within the target region, based on the vertex density and multivariate environmental factors, it is determined whether the grid is to be partitioned. The grid to be partitioned is further partitioned based on the longest axis bisection strategy. Thus, by incorporating vertex density and environmental factors into the determination mechanism of the grid to be partitioned, and recursively partitioning through this mechanism, complex regions or high-risk environments obtain finer grids, while simple regions or low-risk environments maintain coarser grids, achieving non-uniform and differentiated adaptive partitioning.

The invention discloses a sky path planning design method and system based on an artificial intelligence algorithm, and relates to the technical field of unmanned aerial vehicles, and the technical scheme is characterized by comprising the following steps: obtaining environment data of a flight area of an unmanned aerial vehicle, and obtaining environment condition data of the flight area according to the environment data; setting a sub-planning area of the unmanned aerial vehicle according to the environmental condition data, and setting a planning parameter and a planning weight corresponding to the sub-planning area; real-time monitoring parameters of the sub-planning areas are collected, and sub-area planning coefficients of the sub-planning areas are obtained according to the real-time monitoring parameters, the planning parameters and the planning weights; performing comparative analysis on the sub-region planning coefficient and a preset planning coefficient threshold to obtain a first high-risk sub-planning region; obtaining a first high-risk area factor corresponding to the first high-risk sub-planning area; and obtaining the influence area factor of the first high-risk area factor according to the area correlation model, so that the unmanned aerial vehicle can actively avoid the path during planning, and is prevented from falling into a high-risk environment.

The invention discloses a nasal cavity biological protection preparation, a preparation method and application thereof and a nasal cavity intelligent protection system. The formulation comprises a mucin inspired polymer having a specific "brush-like bottleneck" molecular configuration, and an interpenetrating network formed from oppositely charged polysaccharides, which construct a three-dimensional hydrated gel by dynamic non-covalent cross-linking. The gel has the characteristics of shear thinning, strong mucous membrane adhesion and efficient particle interception. Temperature-sensitive, pH-sensitive or oxidation-reduction-sensitive intelligent microspheres can also be integrated in the preparation, so that responsiveness protection to the external environment is realized. The invention further provides an intelligent protection scheme comprising the preparation, a quantitative spraying device and a simple monitoring system. The preparation is safe and free of medicine, is suitable for preventing nasal cavity discomfort caused by air pollution, allergens, viruses and environment dryness, and is especially suitable for daily health protection under high-risk environment or extreme weather conditions.

The invention relates to the technical field of intelligent fire fighting, and discloses an intelligent fire fighting platform multi-equipment information fusion early warning method and system, and the method comprises the steps: continuously collecting the data of detection equipment in a clean room of a semiconductor manufacturing factory, and presetting and storing an abnormal signal sequence template containing ultrafine particles, chemical activity monitoring and traditional fire features; and early recognition of the atypical dangerous events is realized. According to the method, a real-time detection equipment data sequence is compared with an abnormal signal sequence template, the matching degree is obtained, and when the matching degree reaches a preset threshold value, it is judged that an atypical dangerous event exists, and early warning information is generated. According to the invention, the identification capability and the early warning timeliness of the intelligent fire-fighting platform on atypical dangerous events in high-risk environments such as semiconductor manufacturing factories can be obviously improved, and the equipment safety and the personnel health are effectively guaranteed.

The application discloses a kind of service-oriented unmanned equipment intelligent behavior conceptual model construction method, it is related to military field technical field, including the following steps: when unmanned equipment executes reconnaissance and strike task, carries optical, infrared and multispectral camera equipment, battlefield real-time video is continuously collected, through multispectral data acquisition, keep efficient target identification and tracking ability under various complex battlefield environment.The application improves target identification accuracy through multispectral data acquisition, real-time monitoring video stream extracts potential attack features, and with the help of deep learning model, attack hidden danger is efficiently identified, in high-risk environment, self-adapting adjustment detection strategy, enhance the response capability to hidden attack, ensure the accuracy and immediacy of tactical execution, to optimize identification efficiency, reduce the risk of misjudgment and missed detection, improve the utilization rate of combat resources, provide efficient, accurate, adaptive solution for the intelligent application of unmanned equipment.

The utility model provides a steel tray sampling inspection device, which relates to the technical field of sampling inspection, and comprises a steel tray rack, and a standby station is fixedly mounted on the left side of the top of the steel tray rack; and an electric box is fixedly mounted on the rear side of the top of the steel tray rack. According to the remote operation method of the device, the exposure risk of personnel in high-risk environments such as a nuclear warehouse is effectively reduced, the staying time of the personnel in a dangerous area is shortened to the maximum extent, especially when high-risk articles or equipment are processed, the opportunity of direct contact between the personnel and potential dangerous sources is remarkably reduced, and the safety of the personnel is improved. The method solves the problems that when sampling inspection is carried out on special articles such as nuclear barrels, workers have to carry detection devices to go deep into high-risk environments such as nuclear warehouses, the labor intensity of the workers is increased, and more importantly, the workers are directly exposed to various potential hazard sources which possibly exist, so that the safety of the workers is affected. Therefore, the problem of high safety risk is solved.

The application discloses a kind of inspection robots for special environment in coal mine and use method, it is related to coal mine safety production technical field.A kind of inspection robots for special environment in coal mine, including walking track, further include: walking mechanism, main body frame, detection unit, power supply unit, communication unit and holder shield subassembly, wherein, the walking mechanism is cooperated with walking track and realizes movement, the detection unit is realized by holder shield subassembly instrument image acquisition, the power supply unit and communication unit are respectively for whole machine power supply and data transmission;The application ensures stable operation under high-risk environment by adopting explosion-proof safety design, realizes the high-precision positioning and identification of instrument by dynamic search algorithm and intelligent detection algorithm, has unmanned inspection, real-time data transmission function, its modular structure can be flexibly extended, can adapt to a variety of instrument detection needs, significantly improves coal mine inspection efficiency and intelligent level.

The application discloses a kind of mobile security protection method and system based on AI dialogue and context perception, comprising: receiving the natural language request of user, the structured context object of the context information of device is generated by rule engine;Based on natural language request and context object, parse out user intent and at least one entity to be detected, to generate structured security instruction;Call corresponding threat analysis engine to carry out depth analysis and generate threat analysis result;According to threat analysis result, generate composite response containing natural language explanation and one-key execution operation option, call system interface to execute the protection action corresponding to threat analysis result;Collect the feedback data of user to composite response and continuously optimize parsing and threat analysis capability.The application analyzes device state, network environment and user behavior in real time, and the system can dynamically assess security risks, not only respond to user active query, but also actively push protection suggestions when detecting high-risk environment.

This application relates to a method, system, equipment, and medium for assessing the ESG resilience of heating facilities. The method includes: first, acquiring facility type data and dynamic environmental indicators; then, filtering and extracting environmental performance parameters and social demand indicators for heating scenarios to form a preliminary screening dataset; using a random forest algorithm to calculate the impact degree of dynamic environmental factors, obtaining a subset of high-risk environmental performance; extracting governance factor data and training a risk prediction model using a support vector machine algorithm; based on the equilibrium point output by the model, iteratively optimizing to obtain factor balance index data; simultaneously acquiring emission relationship index data and using a random forest algorithm for regression analysis to construct an emission relationship model; finally, extracting allocation fairness features, matching social demand to form an assessment framework, and integrating data to determine the adjustment results for the heating facility resilience assessment. This method can accurately reflect the actual resilience level of heating facilities and improve the scenario adaptability and generalization ability of the assessment method.

To provide a greeting sign display device for a motorcycle that can display a greeting sign with higher visibility than a hand sign to other riders while riding, without taking your hands off the handlebars, even in a high-risk environment such as bad weather or nighttime. [Solution] When riders pass each other, the motorcycle greeting sign display device 10 sends a display instruction signal to the control means 15 when they operate a switch operation unit 14 around the handlebars 13, causing a greeting sign including greeting text to be displayed on the electronic display unit 12. This allows riders to display a highly visible greeting sign by emitting light to other riders, even in high-risk environments such as bad weather or at night, without having to take their hands off the handlebars 13. In addition, the electronic display unit 12 is dustproof and waterproof, so it is difficult for rain while riding, water and condensation when washing the bike, or sand, dirt, fine particles, etc. kicked up while riding to penetrate into the electronic display unit 12.

The present invention discloses a toxic gas detector, which relates to the technical field of toxic gas detection. A negative pressure fan blade and auxiliary blades are arranged in the detection cylinder of the present invention to form a negative pressure flow field. The bottom of the cylinder is adaptively accelerated by a gear ring-planetary control mechanism. The separation dust box adopts a tangential multi-channel air intake and is equipped with an inverted conical sliding funnel to separate particles by centrifugation. The purified gas is sent to the electrochemical sensor group through a coaxial central air intake pipe to achieve highly sensitive monitoring of hydrogen sulfide, carbon monoxide, oxygen, and ammonia. The present invention solves the problems of easy dust clogging, insufficient flow, and cumbersome maintenance of existing detectors, improves detection speed, accuracy, and reliability, and is suitable for continuous safety monitoring in various high-risk environments such as mines, chemical industries, and municipal pipelines.

In one aspect, a computer-implemented method, includes a risk mitigation system receiving from a first sensor mounted on an autonomous vehicle, first sensor data. The first sensor data may be associated with a local magnetic field and is received periodically. The method may further include processing the first sensor data to identify an occurrence of an anomaly in the first sensor data. The occurrence may be associated with a time period. The risk mitigation system may request data from a second sensor mounted on the vehicle after identifying the anomaly. The method may further include receiving, from the second sensor, second sensor data. The second sensor data may be associated with an external environment of the autonomous vehicle. The risk mitigation system may process the second sensor data associated with the time period, and identify, based on the analyzed second sensor data and the anomaly, that the external environment is high-risk.

The invention discloses a service-oriented unmanned equipment intelligent behavior conceptual model construction method, and relates to the technical field of military, and the method comprises the following steps: carrying optical, infrared and multispectral camera equipment when unmanned equipment executes reconnaissance and attack tasks, continuously collecting battlefield real-time videos, and carrying out multi-spectral data collection to obtain an intelligent behavior conceptual model of the unmanned equipment; and high-efficiency target identification and tracking capability can be maintained in various complex battlefield environments. The target identification precision is improved through multispectral data acquisition, the video stream is monitored in real time to extract the potential attack features, the attack hidden danger is efficiently identified by means of a deep learning model, the detection strategy is adaptively adjusted in a high-risk environment, the response capability to the hidden attack is enhanced, the tactical execution precision and instantaneity are ensured, and the method is suitable for popularization and application. Therefore, the identification efficiency is optimized, the misjudgment and leak detection risks are reduced, the utilization rate of combat resources is improved, and an efficient, accurate and self-adaptive solution is provided for intelligent application of unmanned equipment.

The utility model provides a high-temperature and high-pressure ball valve, which relates to the technical field of high-temperature and high-pressure ball valves and comprises a valve body, a valve rod is rotatably connected onto the valve body, a handle is fixedly connected onto the valve rod, a ball body is fixedly connected onto the valve rod, and a fireproof structure is arranged on the valve body. The fireproof structure is mainly composed of two protective shells, the two protective shells are both arranged on the valve body, inorganic fiber layers are fixedly connected to the interiors of the protective shells, aerogel layers are fixedly connected to the inorganic fiber layers, reflecting layers are fixedly connected to the aerogel layers, supporting layers are fixedly connected to the reflecting layers, and the supporting layers are fixedly connected to the fireproof structure. The utility model solves the problems that the connection point of the ball valve is usually one of the weakest links in the system and is easy to become a fire spreading way, and especially in high-risk environments such as petrochemical engineering, power stations, oil refineries and the like, if additional protection is not carried out on the ball valve, the sealing is easy to damage, and the medium is easy to leak.

The invention provides a fire-fighting linkage logic verification method and system based on analog simulation, and the method comprises the steps: obtaining and analyzing a fire-fighting host engineering file selected by a user, obtaining the basic information of a host, a linkage rule, an equipment point table and other information, and in a simulation test interface, based on the host selected by the user and a test mode, verifying the fire-fighting linkage logic. And performing equipment linkage operation according to the linkage logic rule, generating an operation result containing the target output equipment and a linkage instruction, executing the linkage instruction to simulate a real action response of the target output equipment, and displaying a linkage execution result in real time. According to the method, the fire-fighting linkage logic test can be completely carried out in the virtual and simulation environments without actual operation of field entity equipment, the test period is greatly shortened, the test efficiency is improved, the test process is completely carried out in the virtual simulation environment, equipment false triggering and operation in a high-risk environment are avoided, potential safety hazards of field test are eliminated, and the test efficiency is improved. And the equipment loss and the maintenance cost are reduced without depending on field equipment.

The application discloses a coal mine fire prevention early warning method based on intelligent monitoring, and relates to the technical field of coal mine fire prevention early warning, and comprises the following steps: acquiring coal mine environment information data parameters in real time through a coal mine fire prevention early warning system, wherein the environment information data parameters comprise humidity dynamic parameter information and air pressure parameter information. The application intelligently evaluates the environment through the generated environment safety change index; when judging that the environment is high-risk, the system dynamically adjusts the sampling frequency based on a preset sampling frequency, timely captures subtle changes in the early stage of fire such as methane diffusion or air static pressure fluctuation, ensures rapid early warning, improves the accuracy and timeliness of signal capture, and strives for early intervention time for managers. In a low-risk environment, the system collects data at a fixed sampling frequency, reduces unnecessary calculation tasks and energy consumption, supports long-term monitoring and trend analysis. Through the double regulation mechanism, the system guarantees data integrity, optimizes resource utilization and operation efficiency.

The invention provides a power transmission line tower grounding resistance unwiring-free measurement method and system, and belongs to the technical field of power transmission line operation maintenance and detection, and the method comprises the steps: constructing a tower equivalent loop model, employing a non-contact transformer to synchronously inject a test signal, and collecting multi-path response data, and establishing a multi-parameter complex equation set based on the Kirchhoff voltage law for solving, and finally judging the grounding resistance of the tower according to the order of magnitude relationship between the equivalent impedance and the grounding resistance. According to the invention, through the measurement method without wire releasing and auxiliary electrode bonding, the operation steps are simplified, the working efficiency is improved, personnel are prevented from working in a high-risk environment, and the working safety is improved; meanwhile, the portable design provides effective support for data real-time monitoring, online analysis and operation and maintenance decision on the basis of guaranteeing functionality, and the accuracy, efficiency and safety of power transmission line grounding resistance measurement are comprehensively optimized.

The invention discloses a multi-sensitive factor dynamic path optimization method for health protection, and the method comprises the steps: obtaining a user medical file and a medication record, generating a sensitive factor set of a user, and obtaining a candidate path set of user travel; dividing paths in the candidate path set into a plurality of segments according to a road network topological structure, and calculating segment health risk values of the paths based on the sensitive factor set; and constructing a three-dimensional cost function to calculate the total cost of the paths in the candidate path set, solving the path with the lowest total cost by adopting a three-layer path optimization engine, and starting an active protection measure when a health risk threshold is triggered. According to the invention, accurate modeling of individualized health features is realized; the method can remarkably reduce the individual health risk while guaranteeing the travel efficiency, improves the traffic decision intelligence and medical protection capability, and is suitable for the precise health management scene of chronic disease patients, specific sensitive people and high-risk environment travel.

The invention relates to the technical field of intelligent unmanned system autonomous defense, in particular to an LLM-driven situation awareness multi-machine autonomous cooperation method, which comprises the following steps: uniformly converting collected key information of the current environment of each unmanned node at a target moment into a high-dimensional semantic vector; performing joint coding on a natural language instruction semantic vector in the high-dimensional semantic vector and a threat evaluation result, and inputting coded information into a pre-constructed large language model to output a target behavior intention corresponding to each unmanned node; and updating the target behavior intention corresponding to each unmanned node to obtain a cooperative behavior intention corresponding to each unmanned node satisfying a preset condition so as to drive each unmanned node to execute a corresponding target action. Therefore, the problems that unstructured threats cannot be effectively perceived and adapted in a complex dynamic environment, and the survivability and task execution efficiency of an unmanned aerial vehicle system in a high-risk environment are reduced in the prior art are solved.

The invention discloses a self-service camera equipment intelligent optimization shooting system based on deep learning, and the system comprises the steps: collecting environment data, such as illumination intensity, reflectivity, noise level, shooting distance and shooting angle, and generating a training sample set based on the data; constructing a deep reinforcement learning model, and optimizing exposure compensation, focal length adjustment, a shooting angle, a shooting mode and other parameters through multiple rounds of training; a risk sensitive domain randomization technology is adopted, key training is carried out in a high-risk environment, and the adaptability of the model in a complex environment is improved; and deploying the converged exposure control strategy model to equipment, optimizing exposure parameters in real time, and generating a high-quality image. According to the method, through deep reinforcement learning and risk sensitive domain randomization, the method can automatically adapt to environmental changes, the shooting effect is remarkably improved, and especially in a scene with large illumination intensity changes, the quality of the optimized image is greatly improved.

A multi-parameter cable-based linear fire detection system that significantly enhances the capabilities of traditional fire detection systems. This system includes a plurality of sensing modules positioned along the cable, each equipped with a volatile organic compounds (VOC) sensor, a combustible gas sensor, a smoke sensor, an infrared sensor, and linear heat sensors. It also features an addressing module for precise location identification of the fire hazard. The system incorporates an air sampling function with an air tube and multiple inlets to continuously draw and analyze air samples in real-time. By monitoring early indicators such as gas emissions, smoke, and heat, the system ensures comprehensive coverage and timely triggered alarms. The integration of these advanced sensors and functions prevents false positives, provides accurate detection of fire locations, and enhances fire safety in high-risk environments such as battery energy storage facilities and other industrial applications.

The invention discloses a rural road traffic safety risk comprehensive management and evaluation method, which comprises the following steps: dangerous driving behavior identification and disposal training: selecting rural road layout front-end equipment to monitor illegal behaviors, processing the illegal behaviors, dividing risk levels of illegal persons, arranging the illegal persons for training and learning according to the risk levels, and carrying out dangerous driving behavior identification and disposal training; unlearned law violators are persuaded to complete training and learning; high-risk environment monitoring early warning and emergency response are carried out; and checking and evaluating the comprehensive management effect. According to the invention, traffic illegal behaviors of rural roads are monitored in real time and risk personnel are managed in a graded manner, so that the investment of workers and the cost of investigating and dealing with the illegal behaviors are effectively reduced, the rural road environment monitoring and safety early warning emergency response capability is improved, and the intelligent level and safety of traffic management are remarkably improved; the traffic accident rate is reduced, and public travel safety is guaranteed.

The external monitoring frame comprises cabinet connecting rods, a first adjusting assembly, a second adjusting assembly and a monitoring assembly, the cabinet connecting rods are symmetrically arranged on the two sides of the face, needing to be monitored, of the cabinet, the first adjusting assembly is matched with a first adjusting groove in the cabinet connecting rods through a first sliding block, and the second adjusting assembly is matched with a second adjusting groove in the cabinet connecting rods through a second sliding block. The second adjusting assembly can move in the height direction of the cabinet to achieve preliminary longitudinal adjustment, the second adjusting assembly is rotationally and movably connected with the first adjusting assemblies on the two sides, the angle and the position can be flexibly changed, and the monitoring assembly is connected to the second adjusting assembly and right faces the face, needing to be monitored, of the cabinet; according to the utility model, a multi-dimensional adjustable modular structure is adopted, and the monitoring mechanism is laid along the cabinet connecting rod in a sliding manner, so that the position of the camera can be flexibly adjusted in space, cabinets of different specifications can be adapted, and effective monitoring of the to-be-monitored surfaces of the cabinets can be realized; therefore, the problems of high manual maintenance cost and high personnel safety risk of the machine station cabinet in a plateau unmanned area, a remote area and a high-risk environment are effectively solved.

This disclosure relates generally to the fields of data management, collection, conditioning, and curation for driving advanced data analytics, artificial intelligence (AI) and machine learning (ML), and more particularly to contextualized data collection and curation for adaptive learning and for training advanced autonomy models in multi-actor applications in dynamic high-risk environments (e.g., multi-domain socio-technical work environments (MSWEs))—from mission control to military operations, from operating rooms to racecar engineering—where experts must effectively employ technologies to drive data-informed decisions based on situational assessment. This disclosure provides enabling technology for ML-based autonomy solutions to effectively utilize current and emerging analysis and debrief tools across relevant domains, which necessitates an inclusion of formal data contextualization processes. These processes support training the next generation of experts and simultaneously generate relevant contextually labeled data for adaptive learning and training advanced ML models for event-driven autonomy operations.

This invention discloses a protection and control method and system for energy storage converter testing under extreme environments. The method includes calibrating the parameters of a multiphysics reduced-order simulation model based on measured performance data to obtain a calibrated multiphysics reduced-order simulation model; embedding a device-level performance degradation proxy model into the calibrated multiphysics reduced-order simulation model to obtain a system-level simulation model; inputting environmental parameter samples into the system-level simulation model for simulation, obtaining risk assessment results based on the performance simulation result set, and selecting at least one high-risk environmental parameter combination; obtaining corresponding physical test cases, driving test equipment to perform physical extreme environment tests on the energy storage converter, comparing the measured results with the prediction results obtained by the system-level simulation model based on the corresponding high-risk environmental parameter combination simulation, and optimizing the parameters of the multiphysics reduced-order simulation model and the device-level performance degradation proxy model to ensure the reliability of the energy storage converter operating under extreme environments.

Systems and processes are provided for Robust Detection of Accuracy Loss in Explainable AI (XAI) systems using Quantum Entanglement and Model-Matrix Frameworks. The system integrates quantum entanglement principles to detect anomalies in model computations, flagging poisoned data and computational deviations. It uses hash-key tracking not only for final outputs but for the methods used, ensuring any manipulation in processing steps is detected. Real-time continuous scanning monitors for cyberattacks, while a traceability matrix tracks data lineage. The system employs swarm intelligence for decentralized task distribution and auditing, ensuring scalability in large data environments. Additionally, the self-healing capability allows the system to automatically correct identified anomalies, maintaining the reliability of AI outputs. This comprehensive system ensures the integrity, security, and transparency of AI computations, addressing accuracy loss due to both external threats and internal data corruption in high-stakes environments.